Developing User Personas to Aid in the Design of a User-Centered Natural Product-Drug Interaction Information Resource for Researchers

Pharmacokinetic interactions between natural products and conventional drugs can adversely impact patient outcomes. These complex interactions present unique challenges that require clear communication to researchers. We are creating a public information portal to facilitate researchers’ access to credible evidence about these interactions. As part of a user-centered design process, three types of intended researchers were surveyed: drug-drug interaction scientists, clinical pharmacists, and drug compendium editors. Of the 23 invited researchers, 17 completed the survey. The researchers suggested a number of specific requirements for a natural product-drug interaction information resource, including specific information about a given interaction, the potential to cause adverse effects, and the clinical importance. Results were used to develop user personas that provided the development team with a concise and memorable way to represent information needs of the three main researcher types and a common basis for communicating the design’s rationale

A useful tool for drug interaction evaluation: The University of Washington Metabolism and Transport Drug Interaction Database

<p>Abstract</p> <p>The Metabolism and Transport Drug Interaction Database (<url>http://www.druginteractioninfo.org</url>) is a web-based research and analysis tool developed in the Department of Pharmaceutics at the University of Washington. The database has the largest manually curated collection of data related to drug interactions in humans. The tool integrates information from the literature, public repositories, reference textbooks, guideline documents, product prescribing labels and clinical review sections of new drug approval (NDA) packages. The database's easy-to-use web portal offers tools for visualisation, reporting and filtering of information. The database helps scientists to mine kinetics information for drug-metabolising enzymes and transporters, to assess the extent of <it>in vivo </it>drug interaction studies, as well as case reports for drugs, therapeutic proteins, food products and herbal derivatives. This review provides a brief description of the database organisation, its search functionalities and examples of use.</p

DMD066720 83..101

ABSTRACT Regulatory approval documents contain valuable information, often not published, to assess the drug-drug interaction (DDI) profile of newly marketed drugs. This analysis aimed to systematically review all drug metabolism, transport, pharmacokinetics, and DDI data available in the new drug applications and biologic license applications approved by the U.S. Food and Drug Administration in 2014, using the University of Washington Drug Interaction Database, and to highlight the significant findings. Among the 30 new drug applications and 11 biologic license applications reviewed, 35 new molecular entities (NMEs) were well characterized with regard to drug metabolism, transport, and/or organ impairment and were fully analyzed in this review. In vitro, a majority of the NMEs were found to be substrates or inhibitors/inducers of at least one drug metabolizing enzyme or transporter. In vivo, when NMEs were considered as victim drugs, 16 NMEs had at least one in vivo DDI study with a clinically significant change in exposure (area under the time-plasma concentration curve or C max ratio ‡2 or £0.5), with 6 NMEs shown to be sensitive substrates of cytochrome P450 enzymes (area under the time-plasma concentration curve ratio ‡5 when coadministered with potent inhibitors): paritaprevir and naloxegol (CYP3A), eliglustat (CYP2D6), dasabuvir (CYP2C8), and tasimelteon and pirfenidone (CYP1A2). As perpetrators, seven NMEs showed clinically significant inhibition involving both enzymes and transporters, although no clinically significant induction was observed. Physiologically based pharmacokinetic modeling and pharmacogenetics studies were used for six and four NMEs, respectively, to optimize dosing recommendations in special populations and/or multiple impairment situations. In addition, the pharmacokinetic evaluations in patients with hepatic or renal impairment provided useful quantitative information to support drug administration in these fragile populations. Introduction The evaluation of pharmacokinetic drug-drug interactions (DDIs) during the development of a new molecular entity (NME) is based on a systematic and mechanistic approach that includes the assessment of both the possible effect of the NME on other drugs (NME as a perpetrator or precipitant) as well as the effect of other drugs on the NME (NME as a victim or object) s This article has supplemental material available at dmd.aspetjournals.org. ABBREVIATIONS: AUC, area under the time-plasma concentration curve; BCRP, breast cancer resistance protein; BLA, biologic license application; DDI, drug-drug interaction; DIDB, Drug Interaction Database; DME, drug metabolizing enzyme; EM, extensive metabolizer; FDA, Food and Drug Administration; HI, hepatic impairment; HLM, human liver microsome; PXR, pregnane X receptor; IM, intermediate metabolizer; MATE, multidrug and toxin extrusion; MRP, multidrug resistance-associated protein; NDA, new drug application; NME, new molecular entity; OAT, organic anion transporter; OATP, organic anion-transporting polypeptide; OCT, organic cation transporter; P-gp, P-glycoprotein; P450, cytochrome P450; PBPK, physiologically based pharmacokinetic; PGx, pharmacogenetics; PK, pharmacokinetics; PM, poor metabolizer; PMR, postmarketing requirement; RI, renal impairment; TDI, time-dependent inhibition; UGT, UDP-glucuronosyltransferase. 83 and organ impairment modules (http://www.druginteractioninfo.org). All of the parameters were directly extracted from the DIDB, where the changes in mean area under the time-plasma concentration curve (AUC) and maximum plasma concentration (C max ) values were calculated by the DIDB Editorial Team and are presented herein. The DIDB data were curated from a thorough review of the NDA approval packages, including, but not limited to, the product label and clinical pharmacology and biopharmaceutics review for each NDA. The analysis used a mechanistic approach for evaluating DDIs reported for the individual NMEs, based on the decision criteria recommended by the most recent FDA drug interaction guidance document (FDA, 2012). In addition to the individual enzyme and transporter preclinical and clinical studies reported in the NDAs, studies looking at mechanisms for enzyme-transporter interplay, as well as those conducted in diseased populations [e.g., hepatic impairment (HI) or renal impairment (RI)] were also systematically analyzed. The metric used for evaluation of clinical studies are the AUC and C max ratios, defined as AUC inhibited or induced /AUC control and C max, inhibited or induced / C max, control, respectively, with a clinically significant interaction resulting in an AUC or C max ratio 2 (inhibition) or #0.5 (induction). In accordance with the FDA guidance, NMEs were considered weak, moderate, or strong inhibitors or inducers of cytochrome P450 (P450) enzymes when the observed AUC or C max ratios were 1.25-2, 2-5, and 5 for inhibitors, respectively, and 0.5-0.8, 0.2-0.5, and #0.2 for inducers, respectively (FDA, 2012). In addition, important labeling modifications or recommendations were also noted. In 2014, a total of 30 NDAs and 11 BLAs were approved by the FDA. A summary of the NDA/BLAs, including DDIs, PGx, organ impairment studies, physiologically based pharmacokinetic (PBPK) modeling and simulations, as well as therapeutic classes and approval dates, is presented i